摘要
对近年来基于深度学习的单图像三维人体重建的研究现状和发展趋势进行了总结.首先,从模型表示和计算方法两个方面梳理了当前主要的单图像三维人体重建算法.在模型表示上详细介绍了四种常见表示方式及它们之间的相互转化关系,包括深度图像与点云表示、参数化人体模型表示、体素及语义体素表示及隐式曲面函数表示.在计算方法上深入描述了基于上述四种表示方式所提出的算法,并分析了其优缺点;接着,介绍了单图像三维人体重建常用的公共数据集和客观评价指标;然后,在公共数据集上从客观指标和可视化两个角度对当前先进方法进行了评价和对比;最后,在实验结果的基础上总结了当前方法存在的问题和挑战,并展望了单图像三维人体重建未来潜在的研究方向.
Research progress and development tendencies of deep-learning-based single-image 3 dimensions(3D)human reconstruction methods in the past five years were summarized.First,a series of the current state-of-the-art single-image 3D human reconstruction methods were combed from both the perspectives of model representation and computing method.For model representation,the four common representations,including depth image and point cloud representation,parametric body model representation,voxel and semantic voxel representation,and implicit surface function representation,as well as their mutual transformation relationship were presented in detail.For computing method,the proposed algorithms based on the above four representations were deeply described,and their pros and cons were analysed.Subsequently,the publicly available datasets for single-image 3D human reconstruction were introduced,and the quantitative evaluation metrics were presented.Then,the state-ofthe-art single-image 3D human reconstruction methods were evaluated and compared quantitively and qualitatively on publicly available datasets.Finally,based on the experimental results,the problems of the existing methods were presented,and future challenges and research directions of single-image 3D human reconstruction were discussed.
作者
刘乐元
孙见弛
高韵琪
高常鑫
陈靓影
LIU Leyuan;SUN Jianchi;GAO Yunqi;GAO Changxin;CHEN Jingying(National Engineering Research Center for E-Learning,Central China Normal University,Wuhan 430079,China;School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2024年第5期98-122,共25页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金面上资助项目(62077026)
国家自然科学基金重点资助项目(61937001)
中央高校基本科研业务费优秀青年团队资助项目(CCNU22QN012).
关键词
三维着衣人体重建
单图像三维重建
深度学习
点云
体素
参数化模型
隐式曲面函数
混合模型
clothed 3D human reconstruction
single-image 3D reconstruction
deep learning
point cloud
voxel
parametric model
implicit surface function
hybrid model
